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New, automated tool successfully classifies and relates proteins in unprecedented way

a computational strategy for automatically clustering, or grouping, proteins based on SLF similarities and differences. For his study, Murphy used images of randomly chosen, fluorescently labeled proteins. These proteins were produced inside living cells using a technology called CD tagging, which was developed by Jonathan Jarvik and Peter Berget, both associate professors of biological sciences at Carnegie Mellon. The computational analyses were carried out together with Xiang Chen, a graduate student in the Merck Computational Biology and Chemistry program. Chen and Murphy found that the new tool outperformed existing methods of identifying overlapping proteins within cells, such as simple visual categorization of their locations. "Our tool outperformed clustering based on the terms developed by the Gene Ontology Consortium, the best previous way of describing protein location. We found that the Gene Ontology terms were too limited to describe the many complex location patterns we found. Of course, the other drawback of term-based approaches is that they have to be assigned manually by database curators, and this is often not consistent between different curators," said Murphy. Murphy and his colleagues are currently amassing more protein image data using CD Tagging so that they can refine their approach further. They are also working on ways to "train" a general system that will work for different cell types.
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Source:Carnegie Mellon

Related : Journal of Biomedecine and Biotechnology article


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